So sánh phương pháp

	Hỏi đáp bán giám sát	Hỏi đáp Tự giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2006–2020	2019
Người khởi xướng	Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)	Lewis, P.; Alberti, C. et al. (multiple independent groups ~2019)
Loại	Semi-supervised learning applied to extractive/generative QA	Self-supervised NLP training paradigm
Công trình gốc	Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In Proceedings of ICLR 2020.	Lewis, P., Denoyer, L., & Riedel, S. (2019). Unsupervised Question Answering by Cloze Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 4896–4910.
Tên gọi khác	Semi-supervised QA, Self-training for QA, Pseudo-labeled Question Answering, SSL-QA	SSQA, unsupervised question answering, self-supervised QA, zero-label question answering
Liên quan	6	1
Tóm tắt	Semi-supervised question answering (QA) trains a model on a small labeled set of question-answer pairs, then generates pseudo-labels on a large unlabeled corpus and retrains iteratively. This self-training loop dramatically increases effective training data without the cost of full manual annotation, achieving strong performance on reading comprehension, open-domain QA, and machine reading tasks.	Self-supervised Question Answering (SSQA) is a training paradigm that automatically generates question-answer pairs from unlabeled text — using cloze translation, span masking, or neural question generation — to train QA models without any human-labeled data. It enables high-quality reading comprehension systems even when annotated datasets are scarce or domain-specific.